Ciaren

Pre-1.0 · Current release v0.3.0

Where Ciaren is headed

This page describes the direction, not a dated release schedule. For what has shipped, the code is authoritative and releases are tracked on GitHub.

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Release status

Themes, not dates

Current release
v0.3.0
Stage
Pre-1.0

Next

Stable foundationPlugin ecosystemConnectors and data accessExporters and portabilityData qualityMachine learningAI capabilitiesScheduling and automationUser experience and documentation

Project status: pre-1.0 alpha

The public API, data model, and generated code may change between releases, with no backward-compatibility guarantee yet. Use Ciaren for experimentation, prototypes, and controlled internal workflows before relying on it for critical production jobs.

Shipped

Where it is today

The core platform works end to end.

Visual builder

66 transformation nodes plus file, SQL, and cloud-storage I/O: 80 built-in nodes in total.

Multi-engine execution

Polars (default) and pandas, selectable per run.

Data quality contracts

Assert not-null, unique, range, expression, and row-count checks.

Machine learning

Split, feature engineering, train, predict, and evaluate, with MLflow tracking and a Models page.

Scheduling

Built-in cron scheduler with retries, catch-up, and auto-disable.

Plugin platform

Versioned provider contracts, local and entry-point discovery, and signed .ciarenplugin packaging.

Direction

Where it is going

Ciaren aims to become a stable, local-first workflow platform with an extensible ecosystem around it.

Stable foundation

  • Pre-1.0 hardening: stabilize the public API, data model, generated code, and core node behavior before 1.0.
  • Portable flow format: formalize .flow files with schema versioning, JSON Schema, and migrations.
  • Backend source of truth: move node metadata into the backend catalog, consumed via a complete catalog API.

Plugin ecosystem

  • Plugin lifecycle: keep improving discovery, loading, permissions, signature verification, install, update, and uninstall flows.
  • Community distribution: a lightweight index or marketplace for nodes, connectors, templates, exporters, and validators.
  • Production plugin examples: richer patterns beyond the hello-world node, including tests, packaging, and code export.

Connectors and data access

  • More connectors: expand database, file, API, and storage integrations while keeping credentials explicit and local-first.
  • Better browsing: improve remote file and table discovery for SQL, object storage, and local-folder connections.
  • Connection diagnostics: make test results, permission errors, and setup hints more actionable.

Exporters and portability

  • More export targets: explore notebooks, reusable job templates, and other portable artifacts.
  • Export validation: checks that generated artifacts run and match the visual flow's behavior.
  • Reusable handoff: make exported code easier to version, review, and run in another environment.

Data quality

  • Reusable contracts: strengthen validation nodes as first-class data contracts reusable across flows.
  • Quality reports: surface which checks passed or failed per run, with samples of problematic rows.
  • Validation exports: explore exporting quality checks into external test or validation formats.

Machine learning

  • ML workflow maturity: improve metrics, model comparison, lineage, and the Models page.
  • MLflow integration: clearer registered model workflows, aliases, and tracking configuration.
  • Guardrails: stronger warnings for data leakage, risky splits, and fragile training configurations.
  • ML templates: starter flows for common classification, regression, clustering, and feature-engineering tasks.

AI capabilities

  • AI as an extension point: introduced through providers, not as a required dependency of the core app.
  • Assistive workflows: pipeline generation, flow debugging, optimization suggestions, and plain-language error explanations.
  • Data-control safeguards: any AI integration stays explicit about what data is used and how users opt in or out.

Scheduling and automation

  • Schedule observability: better visibility into upcoming runs, missed runs, retries, and auto-disabled schedules.
  • Automation triggers: continue improving REST, CLI, and webhook-based ways to run flows outside the UI.
  • Lightweight orchestration: keep scheduling simple enough for local and self-hosted workflows.

User experience and documentation

  • Debuggability: better per-node errors, preview failures, and flow-level troubleshooting.
  • Onboarding: more demo projects, recipes, screenshots, and sample datasets for real workflows.
  • Contributor paths: make it easier to add nodes, connectors, exporters, validators, docs, and tests.

Boundaries

What Ciaren won't become in the short term

These are today's boundaries, not a lifetime promise. Priorities may shift as the project and community grow, but these are not on the near-term roadmap.

A real-time streaming engine

Batch-style data and ML workflows are where the product is focused for now.

A warehouse-scale orchestrator

Heavy production orchestration belongs in dedicated tools for now. Ciaren exports clean code that runs inside them instead of replacing them.

A multi-tenant SaaS

The core project is local-first and self-hosted. A hosted layer, if it ever ships, would be opt-in and separate from the open-source core.

A black-box runtime

The core stays open-core: the engine and flow logic are inspectable, exportable, and yours to run. A specific plugin (say, a hosted AI feature) may wrap closed server-side logic. That is a plugin-level exception, not how the platform works by default.

Get involved

Shape the roadmap

The direction is decided in public. The best way to influence it is to take part.

Want to help shape this?

Star the repo, open a discussion, or ship a plugin.

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